The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories

2026-07-09

AI Summary

This study investigates whether systematic biases exist in the collective portrayal of queer characters in fictional narratives, with particular attention to the tension between individual characters embodying positive archetypes and overarching narrative tendencies that reinforce negative stereotypes. Innovatively combining archetypometrics with a large-scale LGBTQIA+ character dataset sourced from Fandom, the research quantitatively analyzes the distribution of characters along archetype dimensions such as Hero–Fool. Findings reveal that characters with high queerness scores are frequently assigned positive archetypes like the Hero, yet the broader narrative corpus exhibits a significant skew toward the Fool archetype along queerness-related dimensions. This discrepancy uncovers latent representational biases embedded within collaboratively authored narrative corpora and offers both a novel methodological approach and empirical evidence for understanding collective narrative bias.

Abstract

Visibility in media is pivotal for identity development and for broadening societal views of gender and sexuality. Queer representation has increased in recent years, yet damaging stereotypes and tropes persist. Here, we focus on queer portrayal and its perception by audiences in fictional stories (television, film, and literature) by studying characters by their quantified archetypes which are operationalizations of common conceptions such as Hero, Diva, and Outcast. We use the archetypometrics and Fandom's LGBTQIA+ datasets to study samples of fictional characters along the trait differential spanning straight to queer. We find, quantify, and explain a seeming paradox. The characters with the highest queer score present positive primary archetypes and are typically Heroes rather than Fools, Angels rather than Demons, and Adventurers rather than Traditionalists. But evaluation across many stories for the straight-queer trait itself reveals a strong collective-writing bias towards Fool (away from Hero) and no meaningful loading for the other two dimensions. Our analysis offers a population-scale view of the complexities of queer portrayal, while also pointing to risks in blindly training on many-authored story corpora.

Problem

• queer representation
• archetypometrics
• narrative bias
• fictional characters
• stereotypes

Innovation

• archetypometrics
• queer representation
• narrative bias
• character archetypes
• media stereotypes